PASCAL Workshop on Methods of Data Analysis in Computational Neuroscience and Brain Computer Interfaces

EEG Coupling, Granger Causality and Multivariate Autoregressive Models

author: Alois Schlögl, Fraunhofer FIRST

Categories

Top: Mathematics: Statistics
Top: Medicine

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*Slides*
0:01	EEG Coupling, Granger Causality and Multivariate Autoregressive Models
0:25	Examples of Coupling
1:04	Many Different Coupling Measures Are In Use
1:55	Simulation
3:54	Auto- & Crossspectra pt 1
4:35	Coherency, Coherence (COH)
5:43	Imaginary Coherence (iCOH)
6:49	Phase Differences and Time Delay
8:10	Partial Coherence (pCOH)
9:48	Directed Transfer Function (DTF) pt 1
11:11	Partial Directed Coherence (PDC)
12:37	Which Measure
13:21	Which Measure - PDC
13:37	EEG Data – Motor Imagery
16:54	Auto- & Crossspectra pt 2
18:24	Coherence
19:15	Phase
19:33	Imaginary Coherence
20:41	Partial Coherence
21:09	Directed Transfer Function (DTF) pt 2
21:24	Partial Directed Coherence PDC - Left Hand Imagery
21:57	Partial Directed Coherence PDC - Right Hand Imagery
22:23	All These Coupling Measures Are Based On a Multivariate Autoregressive (MVAR) Model
23:09	Is the Volume Conduction Problem Solved - No
25:49	Gamma Response In Familiar vs Unfamiliar Object Presentation
28:29	MVAR Is More Than MV+AR, CSSP, ICA+FFT, etc
31:09	Comparison of Multivariate Autoregressive (MVAR) Estimators
32:16	Summary
35:35	References
35:39	Thank You

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